Worldwide coastal warming assessment

Rationale

Even though global surface temperature has been steadly increasing at a rate of ~0.2°C per decade in the past 30 years, high-resolution information on the spatial and temporal variability of this phenomenon over large geographical areas was almost absent. A worldwide comparative assessment of the recent warming was urgently needed for coastal areas. These areas, although representing only a small fraction of the World’s oceans, have a major role on global climate forcing, on geochemical cycles, marine ecology and biodiversity, fisheries and economics, to name just a few. This project aimed at quantifying and mapping changes in coastal sea surface temperature (SST) with unprecedented levels of spatial and temporal resolution, worldwide.

Methodology

SST sampling along the coast. NOAA Optimum Interpolation ¼ Degree Daily Sea Surface Temperature Analysis data (also known as Reynolds OI V2 SST data) were acquired from the NOAA´s NCDC webpage. AVHRR data were used due to its greater temporal coverage in relation to AVHRR/AMSRE data.Daily files from 01-01-1982 to 12-31-2011, each bearing daily SSTs and associated standard deviations (SD), were imported into R as georrefered layers and stacked by chronological order. Data were retrieved separately for each coastal pixel (location) across the entire assemblage of temporal layers, worldwide. Coastal pixels were defined as those closer to land but with less than 50% of land contamination, which was assessed using full-resolution GSHHS coastline data.

Calculation of warming rates.For each coastal pixel individually, average warming rates were computed as the slope of the linear regression of seasonally detrended SST vs. time, and expressed as ºC/decade ± s.e.m. To account for errors in the SST OI estimates, regression parameters were estimated using weighted least squares, with weights inversely proportional to the variance of the SST OI. To account for temporal autocorrelation, the degrees of freedom were adjusted using the Quenouille procedure, in which Neffective=N(1-r)/(1+r), with N representing the sample size and r the lag-1 autocorrelation coefficient of the residuals of the seasonally detrended time series.The projected area of each ¼ degree pixel was used to standardize rates of change per unit of area, before reporting average rates of change over regions. Monthly SST changes were calculated in a similar way, but using monthly averages for each location.

Changes in the frequency of extreme hot or cold days. For each location separately, 5 and 95 percentiles of standardized anomalies of the raw SST (1982-2010) were used to define extremely low and extremely high temperature thresholds. Yearly frequencies of daily anomalies exceeding the threshold values were calculated and regressed against time. The Quenouille procedure (see above) was used to correct the degrees of freedom whenever temporal autocorrelation was significant.The figure on the right show the yearly frequency of extreme hot days plotted against time for one of the coastal pixels, yielding the average change in the number of extreme hot events in the last three decades for that location.

Changes in the timing of seasonal warming.This analysis was restricted to temperate latitudes between 60ºS and 30ºS and 30ºN and 60ºN. For each pixel in separate, the first Julian day in each year exceeding the 75th percentile of the entire SST dataset for that geographic location was recorded. For situations in which the Julian date of the first 75th percentile temperature occurred before 1 January (common situation in the southern hemisphere), it was necessary to transform the Julian dates to values < 0 in order to avoid spurious discontinuities in dates associated with the end of the year. These values were then regressed against time, and slopes expressed as the average change in date/decade ± s.e.m. The Quenouille procedure (see above) was used to correct the degrees of freedom when temporal autocorrelation was significant.

Results

Please go to Resources > Data to download a google earth file (.kmz) with the data from this study.